Narrating Canadian War Memorials, Understanding National Identity
Bibliographic record
Abstract
Pierre Berton writes that “Canada, more than most countries, is a nation of … memorials”. Yet, with the passage of time, war memorials inevitably tend to lose their original significance, becoming altogether ‘invisible’ for historically-estranged generations. Hence the need for re-remembering war memorials and monuments for the purposes of consolidating a (national) collective memory. The aim of this paper is a comparative analysis of Fields of Sacrifice (1963, dir. Donald Brittain), Herbert Fairlie Wood’s and John Swettenham’s Silent Witnesses (1974), Robert Shipley’s To Mark Our Place (1987), and Robert Konduras’s and Richard Parrish’s World War I: A Monumental History (2014) within the context of the theoretical distinction between memorial and monument cultures in order to discuss the defining ideological tropes of ‘Canadianness’.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.034 | 0.021 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".